Genetic endowments
and wealth inequality

Daniel Barth, Nicholas W. Papageorge, Kevin Thom
Journal of Political Economy, 2020, vol. 128, No.4

伊藤成朗

Barth, Papageorge, and Thom (2020)

SNP, polygenic score

Genome

Chromosone…DNA

Chromosone…DNA

 

DNA structure

DNA structure

Double helix and rung

Double helix and rung

 

SNP

SNP

  • Chromosomes: 1/2 each from both parents
  • DNA: Rolled up and shapes like an X
  • Gene: DNA segments
  • Genome: 3 billion nucletiodes (letters)
    • Letter sequence = genome sequence
    • 1.5 billion base pairs
  • Base pairs differ between people in less than 1% (15 million) locations
  • Single-nucleotide polymorphism (SNP) = such location
    • 2 chromosomes per person: AT-AT, GC-GC, AT-GC or 0, 2, 1 (reference = GC)

Twin studies

  • Estimate how much genetic factors collectively matter for explaining variations of a trait
    • Collectively: Unit = group level aggregation
    • Explaining variations: Variance decomposition
  • Do not reveal which SNP is correlated

Genome wide association studies (GWASs)

  • Collect \(J\) observable (?) SNPs of individual \(i\).

Regress outcome \(Y_{i}\) on each SNP using \(J\) estimating equations:

\[ Y_{i}=\bfmu'\bfx_{i}+\beta_{j}SNP_{ij}+\epsilon_{ij}, \quad j=1,\dots,J. \]

Polygenic score of \(i\) for the outcome \(Y\) = “Educational attainment (EA) score”

\[ PGS_{i}=\sum_{j=1}^{J}\tilde{\beta} SNP_{ij} \]

  • Use Bayesian LDpred procedure to correct for correlations in \(\tilde{\beta}_{j}\)

  • Use all SNPs: Better out-of-sample results than using only SNPs with genome-wide significance \(p\) value \(< 5*10^{-8} =\) .0000005%

  • PGS is considered to be a predictor of individual fixed effects

    • HRS sample: covariance with EA score / total schooling years variance = 10.6%
    • Edu attainment SNPs \(\propto\) biological process of brain development, cognition (Okbay et al. 2016; Lee et al. 2018)
    • Edu attainment SNPs \(\propto\) cognition SNPs (Okbay et al. 2016)

Interpretation

  • Correlation, not causality: Outcomes ⇐ gene + environment (endogenous)
  • Attenuation due to measurement errors in \(\hat{\beta}_{j}\)
  • Linearity assumption → underestimation of gene contributions (relative to twin studies)
  • Population stratification: Ethnic-group fixed effects ⇐ gene, environment
    • Including principal components of SNP data as covariates is shown to controll geographic variations
  • External validity is up to HRS ancestral composition

Data

Health and retirement survey: Age > 50 + partners, genetic samples 2006, 2008

  • All households with at least one individual with genetic European
  • “Retired households” in 1996, 1998, 2002, 2004, 2006, 2008, 2010
  • Households with 1-2 members, drop same sex HHs
  • Household-year observations of both members 65-75 years old

2590 HHs, 5701 HH-year observations (Table 1)

http://seiroito.github.io/QuartoFiles/posts/BarthPapageorgeThom/BPT.html

Barth, Daniel, Nicholas W. Papageorge, and Kevin Thom. 2020. “Genetic Endowments and Wealth Inequality.” Journal of Political Economy 128 (4): 1474–1522. https://doi.org/10.1086/705415.
Lee, James J, Robbee Wedow, Aysu Okbay, Edward Kong, Omeed Maghzian, Meghan Zacher, Tuan Anh Nguyen-Viet, et al. 2018. “Gene Discovery and Polygenic Prediction from a Genome-Wide Association Study of Educational Attainment in 1.1 Million Individuals.” Nature Genetics 50 (8): 1112–21.
Okbay, Aysu, Jonathan P Beauchamp, Mark Alan Fontana, James J Lee, Tune H Pers, Cornelius A Rietveld, Patrick Turley, et al. 2016. “Genome-Wide Association Study Identifies 74 Loci Associated with Educational Attainment.” Nature 533 (7604): 539.